#! -*- coding: utf-8 -*- # SimCSE 中文测试 # bert4keras链接：https://kexue.fm/archives/8348 # | solution | ATEC | BQ | LCQMC | PAWSX | STS-B | # | SimCSE | 33.90 | 50.29| 71.81 | 13.14 | 71.09 | from bert4torch.snippets import sequence_padding from tqdm import tqdm import numpy as np import scipy.stats from bert4torch.models import build_transformer_model, BaseModel from bert4torch.tokenizers import Tokenizer from bert4torch.snippets import sequence_padding, Callback, get_pool_emb from torch.utils.data import DataLoader from torch import optim, nn import torch from bert4torch.snippets import ListDataset import sys import jieba jieba.initialize() # =============================基本参数============================= model_type, pooling, task_name, dropout_rate = sys.argv[1:] # 传入参数 # model_type, pooling, task_name, dropout_rate = 'BERT', 'cls', 'ATEC', 0.3 # debug使用 print(model_type, pooling, task_name, dropout_rate) assert model_type in {'BERT', 'RoBERTa', 'NEZHA', 'RoFormer', 'SimBERT'} assert pooling in {'first-last-avg', 'last-avg', 'cls', 'pooler'} assert task_name in {'ATEC', 'BQ', 'LCQMC', 'PAWSX', 'STS-B'} if model_type in {'BERT', 'RoBERTa', 'SimBERT'}: model_name = 'bert' elif model_type in {'RoFormer'}: model_name = 'roformer' elif model_type in {'NEZHA'}: model_name = 'nezha' dropout_rate = float(dropout_rate) batch_size = 32 if task_name == 'PAWSX': maxlen = 128 else: maxlen = 64 # bert配置 model_dir = { 'BERT': 'F:/Projects/pretrain_ckpt/bert/[google_tf_base]--chinese_L-12_H-768_A-12', 'RoBERTa': 'F:/Projects/pretrain_ckpt/robert/[hit_torch_base]--chinese-roberta-wwm-ext-base', 'NEZHA': 'F:/Projects/pretrain_ckpt/nezha/[huawei_noah_torch_base]--nezha-cn-base', 'RoFormer': 'F:/Projects/pretrain_ckpt/roformer/[sushen_torch_base]--roformer_v1_base', 'SimBERT': 'F:/Projects/pretrain_ckpt/simbert/[sushen_torch_base]--simbert_chinese_base', }[model_type] config_path = f'{model_dir}/bert_config.json' if model_type == 'BERT' else f'{model_dir}/config.json' checkpoint_path = f'{model_dir}/pytorch_model.bin' dict_path = f'{model_dir}/vocab.txt' data_path = 'F:/Projects/data/corpus/sentence_embedding/' device = 'cuda' if torch.cuda.is_available() else 'cpu' # =============================加载数据集============================= # 建立分词器 if model_type in ['RoFormer']: tokenizer = Tokenizer(dict_path, do_lower_case=True, pre_tokenize=lambda s: jieba.lcut(s, HMM=False)) else: tokenizer = Tokenizer(dict_path, do_lower_case=True) # 读数据 all_names = [f'{data_path}{task_name}/{task_name}.{f}.data' for f in ['train', 'valid', 'test']] print(all_names) def load_data(filenames): """加载数据（带标签） 单条格式：(文本1, 文本2, 标签) """ D = [] for filename in filenames: with open(filename, encoding='utf-8') as f: for l in f: l = l.strip().split('\t') if len(l) == 3: D.append((l[0], l[1], float(l[2]))) return D all_texts = load_data(all_names) train_texts = [j for i in all_texts for j in i[:2]] if task_name != 'PAWSX': np.random.shuffle(train_texts) train_texts = train_texts[:10000] # 加载训练数据集 def collate_fn(batch): texts_list = [[] for _ in range(2)] for text in batch: token_ids = tokenizer.encode(text, maxlen=maxlen)[0] texts_list[0].append(token_ids) texts_list[1].append(token_ids) for i, texts in enumerate(texts_list): texts_list[i] = torch.tensor(sequence_padding(texts), dtype=torch.long, device=device) labels = torch.arange(texts_list[0].size(0), device=texts_list[0].device) return texts_list, labels train_dataloader = DataLoader(ListDataset(data=train_texts), shuffle=True, batch_size=batch_size, collate_fn=collate_fn) # 加载测试数据集 def collate_fn_eval(batch): texts_list = [[] for _ in range(2)] labels = [] for text1, text2, label in batch: texts_list[0].append(tokenizer.encode(text1, maxlen=maxlen)[0]) texts_list[1].append(tokenizer.encode(text2, maxlen=maxlen)[0]) labels.append(label) for i, texts in enumerate(texts_list): texts_list[i] = torch.tensor(sequence_padding(texts), dtype=torch.long, device=device) labels = torch.tensor(labels, dtype=torch.float, device=device) return texts_list, labels valid_dataloader = DataLoader(ListDataset(data=all_texts), batch_size=batch_size, collate_fn=collate_fn_eval) # 建立模型 class Model(BaseModel): def __init__(self, pool_method='cls', scale=20.0): super().__init__() self.pool_method = pool_method with_pool = 'linear' if pool_method == 'pooler' else True output_all_encoded_layers = True if pool_method == 'first-last-avg' else False self.bert = build_transformer_model(config_path, checkpoint_path, model=model_name, segment_vocab_size=0, dropout_rate=dropout_rate, with_pool=with_pool, output_all_encoded_layers=output_all_encoded_layers) self.scale = scale def forward(self, token_ids_list): reps = [] for token_ids in token_ids_list: hidden_state1, pooler = self.bert([token_ids]) rep = get_pool_emb(hidden_state1, pooler, token_ids.gt(0).long(), self.pool_method) reps.append(rep) embeddings_a = reps[0] embeddings_b = torch.cat(reps[1:]) scores = self.cos_sim(embeddings_a, embeddings_b) * self.scale # [btz, btz] return scores def encode(self, token_ids): self.eval() with torch.no_grad(): hidden_state, pooler = self.bert([token_ids]) output = get_pool_emb(hidden_state, pooler, token_ids.gt(0).long(), self.pool_method) return output @staticmethod def cos_sim(a, b): a_norm = torch.nn.functional.normalize(a, p=2, dim=1) b_norm = torch.nn.functional.normalize(b, p=2, dim=1) return torch.mm(a_norm, b_norm.transpose(0, 1)) model = Model(pool_method=pooling).to(device) model.compile(loss=nn.CrossEntropyLoss(), optimizer=optim.Adam(model.parameters(), 1e-5)) class Evaluator(Callback): """评估与保存 """ def __init__(self): self.best_val_consine = 0. def on_epoch_end(self, global_step, epoch, logs=None): val_consine = evaluate(valid_dataloader) if val_consine > self.best_val_consine: self.best_val_consine = val_consine # model.save_weights('best_model.pt') print(f'val_consine: {val_consine:.5f}, best_val_consine: {self.best_val_consine:.5f}\n') def evaluate(dataloader): # 模型预测 # 标准化，相似度，相关系数 sims_list, labels = [], [] for (a_token_ids, b_token_ids), label in tqdm(dataloader): a_vecs = model.encode(a_token_ids) b_vecs = model.encode(b_token_ids) a_vecs = torch.nn.functional.normalize(a_vecs, p=2, dim=1).cpu().numpy() b_vecs = torch.nn.functional.normalize(b_vecs, p=2, dim=1).cpu().numpy() sims = (a_vecs * b_vecs).sum(axis=1) sims_list.append(sims) labels.append(label.cpu().numpy()) corrcoef = scipy.stats.spearmanr(np.concatenate(labels), np.concatenate(sims_list)).correlation return corrcoef if __name__ == '__main__': evaluator = Evaluator() model.fit(train_dataloader, steps_per_epoch=None, epochs=5, callbacks=[evaluator])